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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Rapid Electrochemical Diagnosis of Battery Health and Safety from Cells to Modules

Rapid electrochemical diagnosis of battery health and failure is critical for ensuring reliable battery performance and battery safety. Traditional battery health diagnostics such as capacity measurements and DC pulse tests are reliable and well-understood, however, these measurements of battery capacity and resistance do not capture all aspects of battery degradation. Other aspects of degradation, such as electrolyte decomposition, lithium-plating, and particle cracking are difficult to detect electrochemically but are crucial to measure to get a full picture of battery safety and flag out potential failures. In this work, lab- and field-aged commercial lithium-ion batteries and modules of various chemistries and formats are tested using a variety of traditional electrochemical characterization methods as well as using 2-minute pseudo-random DC pulse sequences at rest and during charge/discharge. The electrochemical measurements are compared to physical cell measurements, cell efficiency, drive cycle performance, physical and thermal heterogeneity, and qualitative safety metrics using statistical and machine-learning methods to discover if a comprehensive "battery health map" can be accurately identified using only rapid DC measurements.

ADVANCED PROPULSION SYSTEMS,ENERGY STORAGE↗
DOE OSTI2023

(Invited) Fuel Cell Component Durability for Million Mile Fuel Cell Trucks

While significant advances have been made and early commercial fuel cell light-duty vehicles (LDVs) are starting to be produced, fuel cells in the heavy-duty-vehicle (HDV) transportation sector (including trucks, long-haul semitrailers, maritime, trains, etc.) are nascent, despite the fact that advantages of fuel cells compared to both diesel and electric powertrains are very compelling in terms of emissions, charging time, efficiency, power-to-weight ratio, among others. However, the fuel-cell technology for HDVs requires a paradigm shift in fuel-cell research and development compared to LDVs, where the emphasis becomes efficiency and improvements in durability instead of a focus on increased power densities and lower cell costs. Heavy-duty applications require significantly longer vehicle lifetimes (>25,000 hours/1,000,000 miles for heavy-duty trucks), and therefore require improved fuel cell durability compared to light-duty vehicles. In 2020, HFTO formed the Million Mile Fuel Cell Truck Consortium (M2FCT) that includes a core team of five national laboratories to overcome durability and efficiency challenges in PEMFCs for heavy-duty applications with an initial focus on long-haul trucks. While targets and testing protocols have been developed for light-duty vehicles, the same level of targets and testing protocols have not been established for heavy-duty transportation. With operational times of greater than > 25,000 hours required, the need for well-developed accelerated stress tests (ASTs) is amplified. Results from these ASTs demonstrate the need for more durable electrode layers and well-controlled potential variations. Finally, this presentation will provide an overview of the DOE's Fuel Cell R&D approach, highlighting past and current activities, as well as strategies to enable fuel cell competiveness for heavy-duty applications.

25 ENERGY STORAGE↗
DOE OSTI2018

Sensitivity of Emissions to Uncertainties in Residual Gas Fraction Measurements in Automotive Engines: A Numerical Study

Initial conditions of the working fluid (air-fuel mixture) within an engine cylinder, namely, mixture composition and temperature, greatly affect the combustion characteristics and emissions of an engine. In particular, the percentage of residual gas fraction (RGF) in the engine cylinder can significantly alter the temperature and composition of the working fluid as compared with the air-fuel mixture inducted into the engine, thus affecting engine-out emissions. Accurate measurement of the RGF is cumbersome and expensive, thus making it hard to accurately characterize the initial mixture composition and temperature in any given engine cycle. This uncertainty can lead to challenges in accurately interpreting experimental emissions data and in implementing real-time control strategies. Quantifying the effects of the RGF can have important implications for the diagnostics and control of internal combustion engines. This paper reports on the use of a well-validated, two-zone quasi-dimensional model to compute the engine-out NO and CO emission in a gasoline engine. The effect of varying the RGF on the emissions under lean, near-stoichiometric, and rich engine conditions was investigated. Numerical results show that small uncertainties (~2–4%) in the measured/computed values of the RGF can significantly affect the engine-out NO/CO emissions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Analysis of Electric Vehicle Charging Behavior Patterns with Function Principal Component Analysis Approach

This manuscript focused on analyzing electric vehicles’ (EV) charging behavior patterns with a functional data analysis (FDA) approach, with the goal of providing theoretical support to the EV infrastructure planning and regulation, as well as the power grid load management. 5-year real-world charging log data from a total of 455 charging stations in Kansas City, Missouri, was used. The focuses were placed on analyzing the daily usage occupancy variability, daily energy consumption variability, and station-level usage variability. Compared with the traditional discrete-based analysis models, the proposed FDA modeling approach had unique advantages in preserving the smooth function behavior of the data, bringing more flexibility in the modeling process with little required assumptions or background knowledge on independent variables, as well as the capability of handling time series data with different lengths or sizes. In addition to the patterns revealed in the EV charging station’s occupancy and energy consumption, the differences between EV driver’s charging time and parking time were analyzed and called for the needs for parking regulation and enforcement. The different usage patterns observed at charging stations located on different land-use types were also analyzed.

Engineering↗
DOE OSTI2016

High-Speed Multiplexed Spatiotemporally Resolved Measurements of Exhaust Gas Recirculation Dynamics in a Multi-Cylinder Engine Using Laser Absorption Spectroscopy

The need for more environmentally friendly and efficient energy conversion is of paramount importance in developing and designing next-generation internal combustion (IC) engines for transportation applications. One effective solution to reducing emissions of mono-nitrogen oxides (NO x ) is exhaust gas recirculation (EGR), which has been widely implemented in modern vehicles. However, cylinder-to-cylinder and cycle-to-cycle variations in the charge-gas uniformity can be a major barrier to optimum EGR implementation on multi-cylinder engines, and can limit performance, stability, and efficiency. Precise knowledge and fine control over the EGR system is thus crucial, particularly for optimizing advanced engine concepts such as reactivity controlled compression ignition (RCCI). An absorption-based laser diagnostic was developed to study spatiotemporal charge-gas distributions in an IC engine intake manifold in real-time. The laser was tuned to an absorption band of carbon dioxide (CO 2 ), a standard exhaust-gas marker, near 2.7 µm. The sensor was capable of probing four separate measurement locations simultaneously, and independently analyzing EGR fraction at speeds of 5 kHz (1.2 crank-angle degree (CAD) at 1 k RPM) or faster with high accuracy. Lastly, the probes were used to study spatiotemporal EGR non-uniformities in the intake manifold and ultimately promote the development of more efficient and higher performance engines.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Reliability of Open Public Electric Vehicle Direct Current Fast Chargers

The aim was to systematically evaluate the usability of all public electric vehicles (EV) direct current fast chargers (DCFC) in the San Francisco region. To achieve a rapid transition to EVs, a highly reliable and easy to use charging infrastructure is critical to building confidence among consumers. The functionality and usability of all 182 open, public DCFC charging stations with CCS connectors (combined charging system) in the 9 counties of the Bay Area were tested (655 electric vehicle service equipment (EVSE) ports). An EVSE was classified as functional if it charged an EV for 2 minutes. Overall, 73.3% of the 655 EVSEs were functional. The causes of the nonfunctioning EVSEs (23.5%) were blank or unresponsive screens or error messages; payment system failures; charge initiation failures; network failures; or broken connectors. In addition, the cable was too short to reach the EV inlet for 3.2% of the EVSEs. A random sampling of 10% of the EVSEs, approximately 8 days after the first evaluation, found no overall change in functionality. The level of functionality found with field testing conflicts with the 95–98% uptime reported by the EV service providers (EVSPs) who operate the EV charging stations. There is a need for precise and verifiable definitions of uptime, downtime, and excluded time, as applied to public EV chargers. In conclusion, the level of failure of the existing public EV DCFC charge infrastructure highlights the importance of improving the system design and maintenance to improve adoption of EVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Network Scale Travel Time Prediction using Deep Learning

In recent years, deep learning models have been receiving increased attention within the artificial intelligence (AI) community because of their high prediction accuracy. In this paper, two deep learning models, long short-term memory (LSTM) and convolutional neural network (CNN), are proposed to predict travel time in a road network. One major advantage of using deep learning for travel time prediction is that it can make accurate predictions for all the segments in the transportation network with a single model structure, instead of building customized models for each segment separately. The proposed models were evaluated on a transportation network in the City of Saint Louis, Missouri. The prediction results show that deep learning can provide accurate prediction for both congested and uncongested traffic conditions, and can successfully capture the traffic dynamics of unexpected incidents or special events. The study findings show that deep learning offers a promising approach to real-time prediction of travel times on a network scale.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Quantitative Evaluation of MD/HD Vehicle Electrification using Statistical Data

This paper presents a wide-ranging analysis of Class 3-8 commercial vehicle electrification by means of developing a framework tool which uses a quantitative method of estimating electric vehicle energy consumption and appropriate charging considerations. The Fleet DNA composite statistics on real-world driving behavior is used to evaluate feasible or market-ready battery electric vehicle (BEV) technologies in medium- and heavy-duty (MD/HD) applications. In the paper, ten representative Class 3-8 commercial vehicle electrifications have been evaluated as a function of various service coverages, including applications in port drayage tractors, refuse trucks, delivery trucks, buses, and bucket trucks. The results indicate significant energy savings and fuel cost savings across all MD/HD vehicle electrifications. The majority of MD BEVs, with the exception of Class 3 bucket trucks, achieve better than a 5-year payback with 50–75% service coverage. For HD BEVs, with the exception of the Class 8 port drayage tractors, the 90% service coverage results in a 10-year or longer payback time, while the 50% service coverage yields a 7–8 year payback. Class 8 port drayage tractors should achieve payback in no more than a 3.5 years with 50–75% service coverage. Thus, the analysis indicates a highly feasible potential for Class 3-6 MD vehicles to be electrified, and feasible opportunities for electrification in Class 7-8 HD short-distance applications.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Navigation Application Programming Interface Route Fuel Saving Opportunity Assessment on Large-Scale Real-World Travel Data for Conventional Vehicles and Hybrid Electric Vehicles

The green-routing strategy instructing a vehicle to select a fuel-efficient route benefits the current transportation system with fuel-saving opportunities. This paper introduces a navigation application programming interface (API), route fuel-saving evaluation framework for estimating fuel advantages of alternative API routes based on large-scale, real-world travel data for conventional vehicles (CVs) and hybrid electric vehicles (HEVs). Navigation APIs, such as Google Directions API, integrate traffic conditions and provide feasible alternative routes for origin-destination pairs. This paper develops two link-based fuel-consumption models stratified by link-level speed, road grade, and functional class (local/non-local), one for CVs and the other for HEVs. The link-based fuel-consumption models are built by assigning travel from many global positioning system driving traces to the links in TomTom MultiNet and road grade data from the U.S. Geological Survey elevation data set. Fuel consumption on a link is computed by the proposed model. This paper envisions two kinds of applications: (1) identifying alternate routes that save fuel, and (2) quantifying the potential fuel savings for large amounts of travel. An experiment based on a large-scale California Household Travel Survey global positioning system trajectory data set is conducted. The fuel consumption and savings of CVs and HEVs are investigated. At the same time, the trade-off between fuel saving and travel time due to choosing different routes is also examined for both powertrains.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green-Routing Applications

A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages roughly one million miles of real-world driving data to generate the estimation model. Driving is categorized at the sub-trip level by average speed, road gradient, and road network geometry, then aggregated by category. An average energy consumption rate is determined for each category, creating an energy rate look-up table. Proposed vehicle trips are then categorized in the same manner, and estimated energy rates are appended from the look-up table. The methodology is robust and applicable to a wide range of driving data. The model has been trained on vehicle travel profiles from the Transportation Secure Data Center at the National Renewable Energy Laboratory and validated against on-road fuel consumption data from testing in Phoenix, Arizona. When compared against the detailed on-road conventional vehicle fuel consumption test data, the energy estimation model accurately predicted which route would consume less fuel over a dozen different tests. When compared against a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology should accurately select the route that consumes the least fuel 90% of the time. The model results can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations to reduce energy consumption. This work provides a highly extensible framework that allows the model to be tuned to a specific driver or vehicle type.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Joint Optimization Scheme for the Planning and Operations of Shared Autonomous Electric Vehicle Fleets Serving Mobility on Demand

As the transportation sector undergoes three major transformations—electrification, shared/on-demand mobility, and automation—there are new challenges to analyzing the impacts of these trends on both the transportation system and the power sector. Most models that analyze the requirements of fleets of shared autonomous electric vehicles (SAEVs) operate at the scale of an urban region, or smaller. A quadratically constrained, quadratic programming problem is formulated, designed to model the requirements of SAEVs at a national scale. The size of the SAEV fleet, the necessary charging infrastructure, the fleet charging schedule, and the dispatch required to serve demand for trips in a region are treated as decision variables. By minimizing both the amortized cost of the fleet and chargers as well as the operational costs of charging, it is possible to explore the coupled interactions between system design and operation. To apply the model at a national scale, key complications about fleet operations are simplified; but a detailed agent-based regional simulation model to parameterize those simplifications is leveraged. Preliminary results are presented, finding that all mobility in the United States (U.S.) currently served by 276 million personally owned vehicles could be served by 12.5 million SAEVs at a cost of $\$ 0.27$/vehicle-mile or $ 0.18/passenger-mile. In conclusion, the energy requirements for this fleet would be 1142 GWh/day (8.5% of 2017 U.S. electricity demand) and the peak charging load 76.7 GW (11% of U.S. power peak). Several model sensitivities are explored, and it is found that sharing is a key factor in the analysis.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Impact of Time-Varying Passenger Loading on Conventional and Electrified Transit Bus Energy Consumption

Transit bus passenger loading changes greatly over the course of a workday. Therefore, time-varying vehicle mass as a result of passenger load becomes an important factor in instantaneous energy consumption. Battery-powered electric transit buses have restricted range and longer 'fueling' time compared with conventional diesel-powered buses; thus, it is vital to know how much energy they require. Our previous work has shown that instantaneous transit bus mass can be obtained by measuring the pressure in the vehicle's airbag suspension system. This report leverages this novel technique to determine the impact of time-varying mass on energy consumption. Sixty-five days of velocity and mass data were collected from in-use transit buses operating on routes in the Twin Cities, MN metropolitan area. The simulation tool Future Automotive Systems Technology Simulator was modified to allow both velocity and mass as time-dependent inputs. This tool was then used to model an electrified and conventional bus on the same routes and determine the energy use of each bus. Results showed that the kinetic intensity varied from 0.27 to 4.69 mi-1 and passenger loading ranged from 2 to 21 passengers. Simulation results showed that energy consumption for both buses increased with increasing vehicle mass. The simulation also indicated that passenger loading has a greater impact on energy consumption for conventional buses than for electric buses owing to the electric bus's ability to recapture energy. This work shows that measuring and analyzing real-time passenger loading is advantageous for determining the energy used by electric and conventional diesel buses.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Feasibility of Operating a Heavy-Duty Battery Electric Truck Fleet for Drayage Applications

Vehicle fleet electrification is regarded as one major pathway toward achieving energy independence and reducing air pollution and greenhouse gas emissions. Compared to light-duty and medium-duty vehicles, electrification of heavy-duty vehicles, especially Class 8 trucks, is more challenging owing to the battery size required to attain the driving range necessary for their operating goals. As drayage trucks generally have a limited daily mileage, return to a home base every night, and spend a large amount of time creeping and idling, drayage operation has been the first targeted application for Class 8 electric trucks. The feasibility of operating battery electric drayage trucks at the individual vehicle level has recently been demonstrated. However, questions remain as to whether these trucks are capable of meeting the needs of typical drayage operation at the fleet level. Here we present a feasibility analysis of operating an electric truck fleet based on real-world operation data of a diesel drayage operator in Southern California. Second-by-second activity data collected from 20 trucks in the fleet were used to estimate the corresponding electric energy consumption and the state of charge of the battery using a microscopic electric energy consumption model. An algorithm for generating tours of drayage activity from the collected data was developed and implemented. Multiple scenarios with different battery charging and truck scheduling assumptions were analyzed. The results show that 85% of the tours could be served by electric trucks if there is opportunity for charging at the home base during the time gap between consecutive tours.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Multiscale and Multivariate Transportation System Visualization for Shopping District Traffic and Regional Traffic

In this paper, we present a suite of visualization techniques for sensor-based transportation system data at different scales to facilitate the exploration of interconnected traffic dynamics at intersections and highways. Additionally, these techniques are designed for analyzing multivariate traffic data from radar-based highway sensors and camera-based intersection sensors recording turn movements and vehicle speed, in the Chattanooga Metropolitan Area, with the capability of (a) revealing multiscale mobility patterns using different levels of data aggregation (e.g., individual sensor for microscale, multiple sensors along a corridor for mesoscale, and a larger number of sensors across the region for macroscale visualization) at different intervals (e.g., 5-min intervals, time of day, full day, and day-of-the-week), and (b) exploring the spatial variation of multiple traffic-related variables (e.g., volumes, speeds, turn movements, and traffic light colors) provided by the sensors. We close with a case study to demonstrate the effectiveness of our multiscale and multivariate visualization techniques. At microscale, we focused on intersection data from a shopping district around Shallowford Road in East Chattanooga. For mesoscale visualization, we studied the Shallowford Road corridor and an adjacent stretch of I-75. At macroscale, we included highway data from the Chattanooga Metropolitan Area. All visualizations were integrated into a web-based situational awareness tool to promote user access and interaction. At a minimum, each visualization provides the option for selecting dates for real-time (depending on sensor availability) and historical data, and additional information on hovering, though most provide more detailed information, including different views of the selected data, or interactive highlights.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Commercial Vehicle Driver Behaviors and Decision Making: Lessons Learned from Urban Ridealongs

As e-commerce and urban deliveries spike, cities grapple with managing urban freight more actively. In order to effectively manage urban deliveries, city planners and policy makers need to better understand driver behaviors and the challenges they experience in performing deliveries. In this study, we collected data on commercial vehicle (CV) driver behaviors by performing ridealongs with various logistics carriers. Ridealongs were performed in Seattle, Washington, covering a range of vehicles (cars, vans, and trucks), goods (parcels, mail, beverages, and printed materials), and customer types (residential, office, large and small retail). Observers collected qualitative observations and quantitative data on trip and dwell times, while also tracking vehicles through GPS devices. The results showed that, on average, urban CVs spent 80 percent of their daily operating time parked. The study also found that, unlike the common belief, drivers (especially those operating heavier vehicles) parked in authorized parking locations, with only less than 5 percent of stops occurring in the travel lane. Dwell times associated with authorized parking locations were significantly longer than those of other parking locations, and mail and heavy goods deliveries generally had longer dwell times. We also identified three main criteria CV drivers used for choosing a parking location: avoiding unsafe maneuvers, minimizing conflicts with other users of the road, and coopetition with other commercial drivers. Here, the results provide estimates for trip times, dwell times, and parking choice types, as well as insights into why those decisions are made and the factors affecting driver choices.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Empirical Estimation of Shortest Route Length along U.S. Interstate Highways Based on Great Circle Distance

In this study, 98 regression models were specified for easily estimating shortest distances based on great circle distances along the U.S. interstate highways nationwide and for each of the continental 48 states. This allows transportation professionals to quickly generate distance, or even distance matrix, without expending significant efforts on complicated shortest path calculations. For simple usage by all professionals, all models are present in the simple linear regression form. Only one explanatory variable, the great circle distance, is considered to calculate the route distance. For each geographic scope (i.e., the national or one of the states), two different models were considered, with and without the intercept. Based on the adjusted R-squared, it was observed that models without intercepts generally have better fitness. Additionally, all these models generally have good fitness with the linear regression relationship between the great circle distance and route distance. At the state level, significant variations in the slope coefficients between the state-level models were also observed. Furthermore, a preliminary analysis of the effect of highway density on this variation was conducted.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Joint Modeling of Access Mode and Parking Choice of Air Travelers Using Revealed Preference Data

Airport ground access mode choice is distinct from everyday mode choice decisions, necessitating context-specific choice model estimation. Understanding airport ground access mode choice decisions is not only important for developing infrastructure planning strategies, but also for assessing the impacts of emerging modes on airport revenues, particularly from parking. However, parking choice is an often-overlooked dimension in airport ground access choice modeling. This paper addresses this gap through the development of a joint model of airport access mode and parking option choice using a passenger survey conducted at Dallas-Fort Worth (DFW) International Airport in 2015. Compared with a traditional conditional logit model that does not consider parking options available at DFW airport, the joint model of mode and parking decisions was found to generate more realistic values of travel time and was shown to have better predictive performance, both of which are critical for obtaining better airport parking revenue estimates and identifying traveler cohorts who may respond more strongly to potential policies targeting curb congestion and parking demand.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.

Severino, Joseph↗